I wish to thank very much Messrs. Smith and Howard of the Boston Safe Deposit and Trust Company for inviting me to speak before you. I am indeed delighted to be here. Nevertheless, I am mindful that I start from a rather disadvantageous position for two reasons « First of all it may be very difficult for me, even under the most auspicious of circumstances, to keep up with the overall tone of pr...

When managing their growing service portfolio, many manufacturers in B2B markets face two significant problems: They fail to communicate the value of their service offerings to their customers, and they lack the capabilities to generate profits with value-added services. To tackle these two issues, we design and evaluate a collaborative filtering recommender system which (a) makes individualize...

Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, called HIN based recommendation. It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommenda...

The introduction of recommender systems to existing online services is now practically inevitable, with the increasing number of items and users on online services. Popular recommender systems have successfully implemented satisfactory systems, which are usually based on collaborative filtering. However, collaborative filtering-based recommenders suffer from well-known problems, such as popular...